Quickly and accurately estimating the location of a thing (e.g., person, vehicle, business asset) in a geographic area can be used to speed up emergency response times, track movement, and link businesses with customers. Most approaches for estimating the location of a thing involve estimating distances between the thing and a set of remote transmitters with known locations and then using geometry to perform the position calculation. The estimated distances are often determined using positioning signals sent between the thing and the transmitters. In many geographical positioning systems, the time of arrival (TOA) of a positioning signal transmitted by a transmitter is measured with precision at a receiver, and that TOA is used to estimate the distance traveled by the positioning signal. In such systems, a time epoch or set of epochs associated with the received signal are the events used for measuring the signal's TOA. TOA positioning systems can be satellite-based, ground-based, airborne-based, or combinations thereof. Examples of such systems including the Global Positioning System (GPS), LORAN, positioning systems based upon timing signals provided by cellular telephone transmitters (e.g., in IS-801, CDMA, and others), and others.
Three main types of TOA positioning systems exist, including: (A) forward trilateration, (B) inverse trilateration, and (C) round trip timing.
In a forward trilateration system, multiple geographically dispersed transmitters send synchronized positioning signals to a receiver so the position of the receiver can be estimated. These signals are often sent concurrently so they have the same transmission time. By measuring the times of arrival of each signal, the receiver's position may be estimated by a geometrical process called trilateration. In doing so, the receiver (or another computing device that estimates the receiver's position) needs to know the positions of the transmitters, and may also need to know the transmission times of the signals, when the transmitters do not transmit the signals with a high degree of synchronization.
In an inverse trilateration system, a signal is transmitted from the unknown location, and that signal is received by geographically dispersed receiving platforms that measure the TOA's of the signal. If these receiver platforms are synchronized to one another, or if their mutual timings are somehow determined, then an estimate of the unknown position may be determined by a geometrical calculation.
In round trip timing systems, the round trip time is measured for a signal that is transmitted by one or more transmitters to a receiver to be located, and in turn retransmitted by the receiver back to the respective transmitters. The reverse is also possible where the signal initially is transmitted by a device at an unknown location, and where the signal is transmitted back to the device by a set of geographically diverse transceiving platforms. A geometrical calculation then determines an estimate of the unknown location.
One significant problem that arises in processing positioning signals from TOA positioning systems is that of multipath, which occurs when a signal from a transmitter arrives at a receiver after reflecting off different buildings and other intermediate objects between the transmitter and the receiver. In many cases, the receiver receives several nearly concurrent, or “multipath”, components of the same signal, which results in a processed signal at the receiver exhibiting a series of pulses of varying amplitudes, phases, and delays that often overlap with one another. In a typical TOA positioning system, it is desirable to determine the TOA of the earliest such pulse, which usually represents a direct path component of the transmitted signal, or at least the shortest multipath component of the signal. In some cases, however, a median of the group of pulses is measured, or some other combination like the leading edge of the composite is utilized. As described later, in many TOA systems a noise-like signal is transmitted and a receiver processes such a signal (e.g., “despreads” it) in order to produce a series of pulses whose timing is more readily determined.
By way of example, FIG. 1A shows an example of a despread signal with no multipath or additive noise present. As shown, FIG. 1A depicts a large peak and very small subsidiary “sidelobes” about this peak. These sidelobes may be present due to the choice of the signal, and/or they may be due to various sources of distortion like bandlimiting filtering, nonlinearities in transceiving circuitry, and other sources. The sidelobes need not be symmetric about the largest peak.
When multipath is present, the peak of the despread signal may be distorted and, furthermore, various subsidiary peaks may occur prior to and after the largest peak. By way of example, FIG. 1B depicts a peak associated with a direct path component as a smaller bump prior to a larger peak associated with a multipath component. Of course, the smaller bump could also be associated with a non-direct multipath component and the true earliest peak may be hidden in noise. It is often the case that a peak associated with a multipath component is greater in magnitude than a peak associated with a direct path component, especially in urban environments. The correlation peaks in FIG. 1B are provided as examples only. In some cases, a peak associated with the direct path component may not be clearly discernable from a peak associated with a multipath component. Also, there are often multiple peaks prior to and after the largest peak.
In positioning systems, a receiver will typically try to determine the TOA of the earliest discernable peak, which is presumed to be associated with the shortest signal path between the receiver and transmitter. Thus, a processing system at the receiver often must determine which of the various subsidiary peaks or waveform “wiggles” prior to the largest peak represent a shorter path component of the signal. In doing so, the system must also distinguish such peaks from those that may be due to some type of system distortion. When performing multiple peak detection, a variety of powerful multipath separation algorithms are sometimes used, such as MUSIC and ESPRIT. However, various sources of system distortion may present themselves as possible earlier peaks, giving rise to errors in the determination of the earliest TOA. One such source of distortion results from signal processing procedures used to remove interfering signals at the receiver.
A problem that plagues many wireless communication systems like TOA positioning systems involves the presence of interfering signals that are observed within the receiving circuitry used by receivers. There are two broad classes of interference: (A) interfering signals due to spurious signals originating within the receiver itself, and (B) interfering signals received by the receiver from external sources. As an example of class (A) interference, various local oscillators within the receiver may provide energy that leaks into the sensitive front ends or other elements of the receiver and appears within the desired signal's received passband. Examples of class (B) interference are weak signals from nearby uncooperative emitters that are permitted within the passband of the desired received signal. Class (B) interference is especially prevalent if operation of the positioning system is within or very near to unlicensed frequency bands. Often, but not always, class (A) interference involves signals that are very narrowband and stable in frequency. In class (B) interference, the interfering signals may have either narrow or wide bandwidth, and may not be stable or predictable. For example, such interference may be FM modulated signals. Either of class (A) interference or class (B) interference, if strong enough and not mitigated, may seriously degrade the sensitivity of a positioning system, which consequentially results in reduced accuracy and may give rise to catastrophic errors.
Thus, it is desirable to remove interfering signals using signal processing methods at the receiver. In communications systems, this may be done in a variety of ways (e.g., filtering or estimation/subtraction). In many cases, the resulting performance is nearly equal to that which is possible without any interference. One primary issue, though, is a reduction in signal-to-noise ratio, which may occasionally increase error rates. In positioning systems, however, the task is not just to detect, demodulate and decode the signal-of-interest, but to precisely measure its TOA. Small distortions incurred as part of the interference mitigation procedure may result in significant decrease in positioning accuracy, particularly in multipath situations. For example, the interference mitigation procedure may produce small subsidiary peaks, or may cause a primary peak to be distorted, either of which may result in TOA estimation errors. Unfortunately, the interference mitigation task is often more difficult for TOA positioning systems.
Various techniques attempt to remove strong interference, including approaches that attempt to fully estimate the interfering signal and subtract this estimated signal from the received composite signal, which is effectively an adaptive interference removal approach. This approach may have difficulty removing the interference when the signal-to-noise ratio is not above a threshold amount or when the interference is variable in nature. One must also consider that various approaches may be complex to implement or may require a significant processing time period.
Accordingly, it is desirable to improve approaches for mitigating interference mitigation in positioning systems that avoid some or all of the limitations described above.